User:99rebound/sandbox

Evaluate Article
Information privacy https://en.wikipedia.org/wiki/Information_privacy


 * Lead Section
 * The lead section for this wiki article is correct in that it contains a brief description of the article's major sections, while not being overly detailed. There is no information that is not present in the article in this section, every thing that is entailed is described later in the article.
 * Content
 * The content seems up to a few years outdated in that there are sources that are most recent from 2018, and in the article there are no mentions of current years with the most recent being 2018. The content that seems to not belong is the last section, "United States Safe harbor program and passenger name record issues." Though issue is about privacy, there previous portions of the article are all general descriptions of the types of privacy accessibility and improvements. The article speaks to the general public with no specificity in group representation. Thus, there is a neutral trend for the ideal of equity gaps, as well as addressing topics related to historically underrecruited populations or topics.
 * Tone and Balance
 * This article is from a neutral point of view. There seems to be no heavily biased claims toward any particular position as most details are regarding factual details rather than solely commentary on the topic of privacy. As a result, no viewpoints are overrepresented or underrepresented nor is there an attempt to persuade the audience toward any direction.
 * Sources and References
 * Yes all facts are backed up with reliable secondary source of information. The sources are all properly cited and footnoted. The sources are relatively current with the most recent being 2018 while oldest being from 2011. The sources are from a variety of authors. The sources used are seen as the most reliable in the searches, with them being well-established organizations such as Cengage Learning. Lastly, the links themselves are functional and take the reader to the correct site.
 * Organization and writing quality
 * Yes the article is well written with formal language and correct punctuation. It is well organized with sections split accordingly to the points of the topic, making it easy to read.
 * Images and Media
 * The article does not include images or image captions as a result.
 * Talk Page discussion
 * There are additional conversations expanding on the topic of information privacy in different regions of the world such as India and China, as well as further comments suggesting improvements to the work such as citations as well as merging topics. This article is rated a C class on the quality scale with high importance. The way Wikipedia discusses this topic differ from the wa we have talked about in class is that theirs is broken down into specific categories such as policies regarding privacy as well as legality, whereas we have only talked about the general subjects and the issues of privacy within those such as healthcare.
 * Overall impressions
 * The articles overall status was sufficient in that it covered all the topics it mentioned in the lead section in depth with proper citations. The strengths include the inclusion of several sources to back up their commentary and claims. The article can improve upon the fact that there were no images to help expand the analysis of this topic of privacy. The article seems to be well developed except that there are no images.

Evaluate Article #2
Cyber-security regulation https://en.wikipedia.org/wiki/Cyber-security_regulation


 * Lead Section
 * The lead section for this wiki article is correct in that it contains a brief description of the article's major sections, while not being overly detailed. There is no information that is not present in the article in this section, everything that is entailed is described later in the article.
 * Content
 * The content seems up to a few years outdated in that there are sources that are most recent from 2015. All sections seem relevant to the topic. The article speaks to the general public with no specificity in group representation. Thus, there is a neutral trend for the ideal of equity gaps, as well as addressing topics related to historically underrecruited populations or topics.
 * Tone and Balance
 * This article is from a neutral point of view. There seems to be no heavily biased claims toward any particular position as most details are regarding factual details rather than solely commentary on the topic of privacy. As a result, no viewpoints are overrepresented or underrepresented nor is there an attempt to persuade the audience toward any direction.
 * Sources and References
 * Yes all facts are backed up with reliable secondary source of information. The sources are all properly cited and footnoted. The sources are a little bit old with the newest to be from 2015. The sources are from a variety of authors. The sources used are seen as the most reliable in the searches, with them being well-established organizations such as Cengage Learning. Lastly, the links themselves are functional and take the reader to the correct site.
 * Organization and writing quality
 * Yes the article is well written with formal language and correct punctuation. It is well organized with sections split accordingly to the points of the topic, making it easy to read.
 * Images and Media
 * The article does not include images or image captions as a result.
 * Talk Page discussion
 * Most of the discussion is about incorporating more sources as well as formatting recommendations. There are not too many that critique or expand on the topic of cybersecurity. This article goes more in depth than we have in class as this whole article is about this topic. The article is rated start-class, with mixed high importance and mid importance for several WikiProjects.
 * Overall impressions
 * The articles overall status was sufficient in that it covered all the topics it mentioned in the lead section in depth with proper citations. The strengths include the inclusion of several sources to back up their commentary and claims. The article can improve upon the fact that there were no images to help expand the analysis of this topic of privacy. The article seems to be well developed except that there are no images. — Preceding unsigned comment added by 99rebound (talk • contribs) 07:01, 23 February 2021 (UTC)

What I plan on contributing to the Local Differential Privacy Article
I plan on including descriptions of different models many researchers have built to attempt to ease the issue of privacy within data systems. In addition, I plan on incorporating different fields other than the IT industry, such as the health industry and how it is also crucial for them to adopt an effective local differential privacy model to protect private and sensitive information of their clients.

Notes for Improving "Local Differential Privacy"
What's missing: Weak lead section, no images and info box to expand information, limited body paragraphs, no subtopics within topics, too short and vague in general

What I plan on adding: I plan on improving and expanding on the importance of local differential privacy by elaborating the threats there are in the current data world, and its use to protect us. This will expand the lead section to an appropriate length and quality. I will also include several formulas, proofs, as well as models that were listed in the sources I found to expand on the visualization aspects of the article with added info boxes to explain the phenomena's of each. The main portion of my contributions will be to add on more details, models, and uses of differential privacy in the body paragraphs. I would also like to add subtopics to analyze specifics of the details I will be sorting through such as the different fields like healthcare that are also deeply effected by privacy threats. All of these contributions should result in the article to be in an adequate length and sufficient information regarding the topic of local differential privacy.

Local differential privacy (LDP) is a model of differential privacy with the added restriction that even if an adversary has access to the personal responses of an individual in the database, that adversary will still be unable to learn too much about the user's personal data. This is contrasted with global differential privacy, a model of differential privacy that incorporates a central aggregator with access to the raw data.

With society growing ever more reliant on the digital world and data driven decision making, the smart devices we all have collect extensive statistics and analysis of our personal data that threatens the privacy of users. The driven data fusion and analysis techniques only exposes the users to become more vulnerable to attacks and disclosure in the big data era. To aid in this privacy concern, local differential privacy is one possible solution. Local differential privacy (LDP) is seen as a widely recognized and prevalent privacy model with distributed architecture which can provide strong privacy guarantees for each user while collecting and analyzing data from privacy leaks on both the client and server side. Furthermore, pendant of any assumptions on the third-party servers, LDP has been imposed as the cutting-edge of research on privacy protection and risen in prominence not only from theoretical interests, but also subsequently from a practical perspective. Due to its powerfulness, LDP has been widely adopted to alleviate the privacy concerns of each user.

History
In 2003, Alexandre V. Evfimievski, Johannes Gehrke, Ramakrishnan Srikant gave a definition equivalent to local differential privacy. In 2008, Kasiviswanathan et al. gave a formal definition conforming with the standard definition of differential privacy.

The prototypical example of a locally differential private mechanism is the randomized response survey technique proposed by Stanley L. Warner in 1965, predating modern discussions of privacy. Warner's innovation was the introduction of the “untrusted curator” model, where the entity collecting the data may not be trustworthy. Before users' responses are sent to the curator, the answers are randomized in a controlled manner guaranteeing differential privacy while allowing valid population-wide statistical inferences.

Applications
The relevance of the ever-growing era of big data calls for high demand of artificial intelligence services at the edges of privacy protection for its users. These services help preserve data privacy in ways that has pushed the research on novel machine learning paradigms that fit their requirements.

Federated Learning (FL)
Federated learning has the ambition to protect data privacy through distributed learning methods that keep the data in its storage. Likewise, differential privacy (DP) attains to improve the protection of data privacy by measuring the privacy loss in the communication among the elements of federated learning. The prospective matching of federated learning and differential privacy to the challenges of data privacy protection has caused the release of several software tools that support their functionalities, but they lack a unified vision of these techniques, and a methodological workflow that supports their usage. In the study sponsored by the Andalusian Research Institute in Data Science and computational Intelligence, they developed a Sherpa.ai FL, 1,2 which is an open-research unified FL and DP framework that aims to foster the research and development of AI services at the edges and to preserve data privacy. The characteristics of FL and DP tested and summarized in the study suggests that they make them good candidates to support AI services at the edges and to preserve data privacy through their finding that by setting the value of $$\epsilon$$ for lower values would guarantee higher privacy at the cost of lower accuracy.

Health Data Aggregation
The rise of technology not only changes the way we work and perform our everyday lives, but also the changes to the health industry is also prominent as a result of the rise of the big data era is emphasized. The rapid growth of the health data scale, the limited storage and computation resources of wireless body area sensor networks is becoming a barrier to the development of the health industry to keep up. Aiming to solve this, the outsourcing of encrypted health data to the cloud has been an appealing strategy. However, there may come potential downsides as do all choices. The data aggregation will become more difficult and more vulnerable to data branches of this sensitive information of the patients of the healthcare industry. In his academic article, "Privacy-Enhanced and Multifunctional Health Data Aggregation under Differential Privacy Guarantees," Hao Ren and his team proposes a privacy enhanced and multifunctional health data aggregation scheme (PMHA-DP) under differential privacy. This aggregation function is designed to protect the aggregated data from cloud servers. The performance and evaluation done in their study shows that the proposal leads to less communication overhead than the existing data aggregation models currently in place.

Definition of ε-local differential privacy
Let ε be a positive real number and $$\mathcal{A}$$ be a randomized algorithm that takes a user's private data as input. Let $$\textrm{im} \mathcal{A}$$ denote the image of $$\mathcal{A}$$. The algorithm $$\mathcal{A}$$ is said to provide $$\epsilon$$-local differential privacy if, for all pairs of user's possible private data $$x$$ and $$x^\prime$$ and all subsets $$S$$ of $$\textrm{im} \mathcal{A}$$: $$\Pr[\mathcal{A}(x) \in S] \leq e^{\epsilon} \times \Pr[\mathcal{A}(x^\prime) \in S],$$ where the probability is taken over the randomness used by the algorithm.

The main difference between this definition and the standard definition of differential privacy is that in differential privacy the probabilities are of the outputs of an algorithm that takes all users' data and here it is on an algorithm that takes a single user's data.

Sometimes the definition takes an algorithm that has all users data as input, and outputs a collection of all responses (such as the definition in Raef Bassily, Kobbi Nissim, Uri Stemmer and Abhradeep Guha Thakurta's 2017 paper ).

Deployment
Local differential privacy has been deployed in several internet companies:• RAPPOR, where Google used local differential privacy to collect data from users, like other running processes and Chrome home pages

• Private Count Mean Sketch (and variances) where Apple used local differential privacy to collect emoji usage data, word usage and other information from iPhone users